update docstring

Signed-off-by: KumoLiu <[email protected]>

monai/data/grid_dataset.py

@@ -196,6 +196,25 @@ class GridPatchDataset(IterableDataset):
             see also: :py:class:`monai.data.PatchIter` or :py:class:`monai.data.PatchIterd`.
         transform: a callable data transform operates on the patches.
         with_coordinates: whether to yield the coordinates of each patch, default to `True`.
+        cache: whether to use cache mache mechanism, default to `False`.
+            see also: :py:class:`monai.data.CacheDataset`.
+        cache_num: number of items to be cached. Default is `sys.maxsize`.
+            will take the minimum of (cache_num, data_length x cache_rate, data_length).
+        cache_rate: percentage of cached data in total, default is 1.0 (cache all).
+            will take the minimum of (cache_num, data_length x cache_rate, data_length).
+        num_workers: the number of worker threads if computing cache in the initialization.
+            If num_workers is None then the number returned by os.cpu_count() is used.
+            If a value less than 1 is specified, 1 will be used instead.
+        progress: whether to display a progress bar.
+        copy_cache: whether to `deepcopy` the cache content before applying the random transforms,
+            default to `True`. if the random transforms don't modify the cached content
+            (for example, randomly crop from the cached image and deepcopy the crop region)
+            or if every cache item is only used once in a `multi-processing` environment,
+            may set `copy=False` for better performance.
+        as_contiguous: whether to convert the cached NumPy array or PyTorch tensor to be contiguous.
+            it may help improve the performance of following logic.
+        hash_func: a callable to compute hash from data items to be cached.
+            defaults to `monai.data.utils.pickle_hashing`.
 
     """
 
@@ -305,6 +324,14 @@ def _load_cache_item(self, idx: int):
         return patch_cache
 
     def _generate_patches(self, src, **apply_args):
+        """
+        yield patches optionally post-processed by transform.
+
+        Args:
+            src: a iterable of image patches.
+            apply_args: other args for `self.patch_transform`.
+
+        """
         for patch, *others in src:
             out_patch = patch
             if self.patch_transform is not None:
@@ -343,7 +370,7 @@ def __iter__(self):
 
 class PatchDataset(IterableDataset):
     """
-    returns a patch from an image dataset.
+    Yields patches from data read from an image dataset.
     The patches are generated by a user-specified callable `patch_func`,
     and are optionally post-processed by `transform`.
     For example, to generate random patch samples from an image dataset: